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A Hough transform global probabilistic approach to multiple-subject diffusion MRI tractography.

Iman Aganj1, Christophe Lenglet, Neda Jahanshad

  • 1Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis, 200 Union St. SE, MN 55455, USA. iman@umn.edu

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|March 8, 2011
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This study introduces a novel global probabilistic fiber tracking method using Hough transform voting to identify brain connections. This approach efficiently generates population-representative tracts from multiple subjects without individual alignment.

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Area of Science:

  • Neuroimaging
  • Computational Neuroscience
  • Medical Image Analysis

Background:

  • Accurate brain white matter tractography is crucial for understanding neural connectivity.
  • Existing methods often struggle with local minima and require individual subject alignment for population studies.

Purpose of the Study:

  • To develop a robust global probabilistic fiber tracking framework.
  • To enable efficient generation of population-representative tracts from multiple subjects.

Main Methods:

  • A Hough transform-based voting process to score candidate 3D curves in diffusion MRI data.
  • An exhaustive search strategy to avoid local minima.
  • Non-linear combination of registered high-angular resolution diffusion images (HARDI) from multiple subjects into a single representative volume.

Main Results:

  • The proposed method successfully identifies anatomical connections by selecting high-scoring curves.
  • Population-representative tracts were generated by running the algorithm once on a combined multi-subject volume.
  • The approach was validated on diverse datasets, including simulated, phantom, and human/monkey brain HARDI data.

Conclusions:

  • The Hough transform-based global probabilistic fiber tracking offers an efficient and robust method for neuroanatomical connectivity analysis.
  • This technique simplifies multi-subject tractography by eliminating the need for individual tract alignment.
  • The framework demonstrates broad applicability across various HARDI datasets and imaging resolutions.